Surface temperatures

Visualizing the surface temperature change for the Northern Hemisphere for past years.

Importing libraries and packages

# Warnings
import warnings

# Mathematical operations and data manipulation
import pandas as pd

# Plotting
import matplotlib.pyplot as plt
import seaborn as sns

sns.set()
warnings.filterwarnings("ignore")

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

dataset = pd.read_csv(
    f"{data_path}/northern_surface_temperature.csv", index_col=["Year"]
)

Exploring dataset

# Shape of the dataset
print("Shape of the dataset: ", dataset.shape)
# View
dataset

Shape of the dataset:  (139, 12)

	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
Year
1880	-0.34	-0.50	-0.23	-0.29	-0.05	-0.15	-0.17	-0.25	-0.22	-0.30	-0.42	-0.39
1881	-0.30	-0.21	-0.03	0.01	0.04	-0.32	0.09	-0.03	-0.25	-0.42	-0.35	-0.22
1882	0.28	0.23	0.03	-0.30	-0.24	-0.28	-0.26	-0.13	-0.23	-0.51	-0.31	-0.67
1883	-0.56	-0.64	-0.14	-0.29	-0.24	-0.10	-0.04	-0.21	-0.32	-0.15	-0.44	-0.14
1884	-0.16	-0.11	-0.63	-0.59	-0.36	-0.40	-0.44	-0.49	-0.44	-0.43	-0.55	-0.46
...	...	...	...	...	...	...	...	...	...	...	...	...
2014	0.97	0.71	1.20	1.08	0.90	0.81	0.76	0.89	0.83	0.93	0.79	1.10
2015	1.19	1.18	1.32	1.03	1.04	1.05	0.92	1.01	1.16	1.29	1.37	1.53
2016	1.55	1.93	1.91	1.49	1.11	1.12	1.04	1.11	1.22	1.03	1.11	1.03
2017	1.39	1.50	1.51	1.20	0.92	0.93	0.96	1.01	1.00	1.05	1.23	1.40
2018	1.14	1.23	1.18	0.97	1.01	0.86	0.90	0.86	0.96	1.23	0.98	1.09

139 rows × 12 columns

Preprocessing

# Transpose the dataset
dataset = dataset.transpose()
dataset

Year	1880	1881	1882	1883	1884	1885	1886	1887	1888	1889	...	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
Jan	-0.34	-0.30	0.28	-0.56	-0.16	-0.99	-0.71	-1.05	-0.46	-0.25	...	0.83	0.81	0.61	0.67	0.86	0.97	1.19	1.55	1.39	1.14
Feb	-0.50	-0.21	0.23	-0.64	-0.11	-0.43	-0.80	-0.67	-0.57	0.33	...	0.70	0.94	0.57	0.59	0.79	0.71	1.18	1.93	1.50	1.23
Mar	-0.23	-0.03	0.03	-0.14	-0.63	-0.21	-0.69	-0.41	-0.60	0.00	...	0.60	1.10	0.88	0.72	0.81	1.20	1.32	1.91	1.51	1.18
Apr	-0.29	0.01	-0.30	-0.29	-0.59	-0.47	-0.34	-0.35	-0.18	0.18	...	0.70	1.13	0.85	1.07	0.68	1.08	1.03	1.49	1.20	0.97
May	-0.05	0.04	-0.24	-0.24	-0.36	-0.56	-0.30	-0.22	-0.13	-0.01	...	0.66	0.94	0.66	0.97	0.78	0.90	1.04	1.11	0.92	1.01
Jun	-0.15	-0.32	-0.28	-0.10	-0.40	-0.42	-0.34	-0.18	0.00	-0.05	...	0.68	0.85	0.76	0.94	0.77	0.81	1.05	1.12	0.93	0.86
Jul	-0.17	0.09	-0.26	-0.04	-0.44	-0.31	-0.11	-0.21	0.03	-0.06	...	0.68	0.89	0.73	0.85	0.70	0.76	0.92	1.04	0.96	0.90
Aug	-0.25	-0.03	-0.13	-0.21	-0.49	-0.37	-0.40	-0.51	-0.18	-0.18	...	0.72	0.87	0.71	0.76	0.73	0.89	1.01	1.11	1.01	0.86
Sep	-0.22	-0.25	-0.23	-0.32	-0.44	-0.36	-0.30	-0.18	-0.17	-0.28	...	0.85	0.71	0.74	0.86	0.70	0.83	1.16	1.22	1.00	0.96
Oct	-0.30	-0.42	-0.51	-0.15	-0.43	-0.33	-0.28	-0.46	0.00	-0.39	...	0.79	0.89	0.91	0.91	0.83	0.93	1.29	1.03	1.05	1.23
Nov	-0.42	-0.35	-0.31	-0.44	-0.55	-0.35	-0.37	-0.24	0.03	-0.60	...	0.85	1.21	0.66	0.98	1.13	0.79	1.37	1.11	1.23	0.98
Dec	-0.39	-0.22	-0.67	-0.14	-0.46	-0.08	-0.18	-0.41	-0.22	-0.53	...	0.67	0.50	0.88	0.46	0.83	1.10	1.53	1.03	1.40	1.09

12 rows × 139 columns

Visualisation

heat_colormap = sns.diverging_palette(240, 15, s=99, as_cmap=True)
# Plotting the heatmap for every 5 years
plt.figure(dpi=200)
# To ensure the neutral colour corresponds to no temperature change (= 0),
# set center=0
sns.heatmap(dataset.iloc[:, ::5], cmap=heat_colormap, center=0)
plt.title("Temperature Changes from 1880 to 2015 (base period 1951-1980)")
plt.show()